Investigating the Association of Subjective Numeracy, Interpersonal Communication, and Perceived Discrimination With Watching Health-Related Videos on Social Media Platforms: Cross-Sectional Analysis
Notice bibliographique
Résumé
Background: Over the past two decades, use of social media has grown among US adults. Common social media platforms include Facebook, YouTube, Instagram, X, LinkedIn, and TikTok. People proactively use social media for a variety of purposes including searching for health information, peer-to-peer social support, and health-related information sharing. As these platforms often serve as sources of health information, understanding how, if at all, people use them may inform future behavioral interventions delivered via social media. Additionally, a better understanding of social engagement may have implications for public health messaging and patient-centered communication. Objective: Using a nationally representative sample of US adults, we explored how factors including subjective numeracy (ie, ease of understanding medical statistics), interpersonal communication with family and friends, and perceived discrimination influence whether people ever watched versus never watched health-related videos on social media platforms. Methods: We analyzed the National Cancer Institute's Health Information National Trends Survey data, which were collected from March to November 2022 (n=6252). After excluding participants who did not have complete data for all variables of interest, we analyzed responses from 4543 participants. Respondents were asked, "In the past 12 months, how often did you watch a health-related video on a social media site (eg, YouTube)?" Response options included: almost every day, at least once a week, a few times a month, less than once a month, and never. We collapsed answers into ever or never watched. Odds ratios (OR), 95% CIs, and P values were calculated. A multivariate logistic regression model was considered using all factors that were univariately significant (P<.10). Using backward elimination, factors that were not significant with P>.05 were removed one by one until remaining factors were all significant collectively (P<.05). Results: Of 4543 adults analyzed, 61.5% reported watching at least one health-related video in the past 12 months, whereas 38.5% had never watched one. In the multivariable analysis, all age group categories over 50 years were less likely to watch health-related videos compared to those aged 18-34 years, with respondents aged ≥75 years having the lowest odds of all groups for watching a health-related video (OR 0.16, P<.001). Higher odds of watching health-related videos were observed among respondents who were Black (OR 1.59, P<.01), Hispanic (OR 1.54, P=.01), and from "Other" minority groups (OR 2.07, P=.01) compared to White respondents. College graduates (OR 1.71, P<.01) and those who found medical statistics easy to understand (OR 1.29, P=.04), talked about health with friends or family (OR 1.68, P<.01), or experienced racial discrimination in medical care (OR 1.59, P=.02) also had higher odds of watching health-related videos on social media. Conclusions: Findings from this study may help target health communication campaigns on social media designed to improve screening, lifestyle changes, medication adherence, and disease management.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».